Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Value of Multimodal Diffusion-weighted Imaging in Preoperative Evaluation of Ki-67 Expression in Endometrial Carcinoma

Author(s): Huan Meng, Si-Xuan Ding, Yu Zhang, Feng-Ying Zhu, Jing Wang, Jia-Ning Wang*, Bu-Lang Gao* and Xiao-Ping Yin*

Volume 20, 2024

Published on: 06 October, 2023

Article ID: e110823219686 Pages: 8

DOI: 10.2174/1573405620666230811142710

Price: $65

Abstract

Purpose: To investigate the value of multimodal diffusion weighted imaging (DWI) in preoperative evaluation of Ki-67 expression of endometrial carcinoma (EC).

Materials and Methods: Patients who had undergone pelvic DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) sequence MRI scan before surgery were retrospectively enrolled. Single index model, double index model, and DKI were used for post-processing of the DWI data, and the apparent diffusion coefficient (ADC), real diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), non-Gaussian mean diffusion kurtosis (MK), mean diffusion coefficient (MD) and anisotropy fraction (FA) were calculated and compared between the Ki-67 high (≥50%) and low (<50%) expression groups.

Results: Forty-two patients with a median age of 56 (range 37 - 75) years were enrolled, including 15 patients with a high Ki-67 (≥50%) expression and 27 with a low Ki-67 (<50%) expression. The MK (0.91 ± 0.12 vs. 0.76 ± 0.12) was significantly (P<0.05) higher while MD (0.99 ± 0.17 vs. 1.16 ± 0.22), D (0.55 ± 0.06 vs. 0.62 ± 0.08), and f (0.21 vs. 0.28) were significantly (P<0.05) lower in the high than in the low expression group. The combined model of MK, MD, D, and f-values had the largest area under the curve (AUC) value of 0.869 (95% CI: 0.764-0.974), sensitivity 0.733 and specificity 0.852, followed by the MK value with an AUC value 0.827 (95% CI: 0.700-0.954), sensitivity 0.733 and specificity 0.815.

Conclusions: IVIM and DKI have certain diagnostic values for preoperative evaluation of the EC Ki-67 expression, and the combined model has the highest diagnostic efficiency.

[1]
Islami F, Ward EM, Sung H, et al. Annual report to the nation on the status of cancer, Part 1: National cancer statistics. J Natl Cancer Inst 2021; 113(12): 1648-69.
[http://dx.doi.org/10.1093/jnci/djab131] [PMID: 34240195]
[2]
Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin 2021; 71(1): 7-33.
[http://dx.doi.org/10.3322/caac.21654] [PMID: 33433946]
[3]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN Estimates of Incidence and Mortality worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021; 71(3): 209-49.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[4]
Kitson S, Sivalingam VN, Bolton J, et al. Ki-67 in endometrial cancer: Scoring optimization and prognostic relevance for window studies. Mod Pathol 2017; 30(3): 459-68.
[http://dx.doi.org/10.1038/modpathol.2016.203] [PMID: 27910946]
[5]
Xu Q, Chen C, Liu B, et al. Association of iRhom1 and iRhom2 expression with prognosis in patients with cervical cancer and possible signaling pathways. Oncol Rep 2020; 43(1): 41-54.
[PMID: 31661139]
[6]
Ocak B, Atalay FÖ, Sahin AB, et al. The impact of Ki-67 index, squamous differentiation and several clinicopathologic parameters on the recurrence of low and intermediate-risk endometrial cancer. Bosn J Basic Med Sci 2021; 21(5): 549-54.
[http://dx.doi.org/10.17305/bjbms.2020.5437] [PMID: 33714260]
[7]
Filipov Peres G, Spadoto-Dias D, Neves Bueloni-Dias F, et al. Immunohistochemical expression of hormone receptors, Ki-67, endoglin (CD105), claudins 3 and 4, MMP -2 and -9 in endometrial polyps and endometrial cancer type I. OncoTargets Ther 2018; 11: 3949-58.
[http://dx.doi.org/10.2147/OTT.S160014] [PMID: 30022838]
[8]
Garcia TS, Appel M, Rivero R, Kliemann L, Wender MCO. Agreement between preoperative endometrial sampling and surgical specimen findings in endometrial carcinoma. Int J Gynecol Cancer 2017; 27(3): 473-8.
[http://dx.doi.org/10.1097/IGC.0000000000000922] [PMID: 28187097]
[9]
Nougaret S, Horta M, Sala E, et al. Endometrial Cancer MRI staging: Updated guidelines of the european society of urogenital radiology. Eur Radiol 2019; 29(2): 792-805.
[http://dx.doi.org/10.1007/s00330-018-5515-y] [PMID: 29995239]
[10]
Maciel C, Bharwani N, Kubik-Huch RA, et al. MRI of female genital tract congenital anomalies: European society of urogenital radiology (ESUR) guidelines. Eur Radiol 2020; 30(8): 4272-83.
[http://dx.doi.org/10.1007/s00330-020-06750-8] [PMID: 32221681]
[11]
Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: Application to diffusion and perfusion in neurologic disorders. Radiology 1986; 161(2): 401-7.
[http://dx.doi.org/10.1148/radiology.161.2.3763909] [PMID: 3763909]
[12]
Wang W, Zhang X, Zhu L, et al. Prediction of prognostic factors and genotypes in patients with breast cancer using multiple mathematical models of mr diffusion imaging. Front Oncol 2022; 12825264
[http://dx.doi.org/10.3389/fonc.2022.825264] [PMID: 35174093]
[13]
Chryssou EG, Manikis GC, Ioannidis GS, et al. Diffusion weighted imaging in the assessment of tumor grade in endometrial cancer based on intravoxel incoherent motion MRI. Diagnostics 2022; 12(3): 692.
[http://dx.doi.org/10.3390/diagnostics12030692] [PMID: 35328246]
[14]
Meng N, Fang T, Feng P, et al. Amide proton transfer-weighted imaging and multiple models diffusion-weighted imaging facilitates preoperative risk stratification of early-stage endometrial carcinoma. J Magn Reson Imaging 2021; 54(4): 1200-11.
[http://dx.doi.org/10.1002/jmri.27684] [PMID: 33991377]
[15]
Satta S, Dolciami M, Celli V, et al. Quantitative diffusion and perfusion MRI in the evaluation of endometrial cancer: validation with histopathological parameters. Br J Radiol 2021; 94(1125): 20210054.
[http://dx.doi.org/10.1259/bjr.20210054] [PMID: 34111974]
[16]
Zhang Q, Ouyang H, Ye F, et al. Multiple mathematical models of diffusion-weighted imaging for endometrial cancer characterization: Correlation with prognosis-related risk factors. Eur J Radiol 2020; 130109102
[http://dx.doi.org/10.1016/j.ejrad.2020.109102] [PMID: 32673928]
[17]
Song JC, Lu SS, Zhang J, Liu XS, Luo CY, Chen T. Quantitative assessment of diffusion kurtosis imaging depicting deep myometrial invasion: A comparative analysis with diffusion-weighted imaging. Diagn Interv Radiol 2020; 26(2): 74-81.
[http://dx.doi.org/10.5152/dir.2019.18366] [PMID: 32071025]
[18]
Yamada I, Sakamoto J, Kobayashi D, et al. Diffusion kurtosis imaging of endometrial carcinoma: Correlation with histopathological findings. Magn Reson Imaging 2019; 57: 337-46.
[http://dx.doi.org/10.1016/j.mri.2018.12.009] [PMID: 30599199]
[19]
Yue W, Meng N, Wang J, et al. Comparative analysis of the value of diffusion kurtosis imaging and diffusion-weighted imaging in evaluating the histological features of endometrial cancer. Cancer Imaging 2019; 19(1): 9.
[http://dx.doi.org/10.1186/s40644-019-0196-6] [PMID: 30764876]
[20]
Jiang JX, Zhao JL, Zhang Q, et al. Endometrial carcinoma: Diffusion-weighted imaging diagnostic accuracy and correlation with Ki-67 expression. Clin Radiol 2018; 73(4): 413.e1-6.
[http://dx.doi.org/10.1016/j.crad.2017.11.011] [PMID: 29246587]
[21]
Li Y, Lin CY, Qi YF, et al. Three-dimensional turbo-spin-echo amide proton transfer-weighted and intravoxel incoherent motion MR imaging for type I endometrial carcinoma: Correlation with Ki-67 proliferation status. Magn Reson Imaging 2021; 78: 18-24.
[http://dx.doi.org/10.1016/j.mri.2021.02.006] [PMID: 33556484]
[22]
Fu F, Meng N, Huang Z, et al. Identification of histological features of endometrioid adenocarcinoma based on amide proton transfer-weighted imaging and multimodel diffusion-weighted imaging. Quant Imaging Med Surg 2022; 12(2): 1311-23.
[http://dx.doi.org/10.21037/qims-21-189] [PMID: 35111626]
[23]
Meng N, Wang X, Sun J, et al. Evaluation of amide proton transfer-weighted imaging for endometrial carcinoma histological features: A comparative study with diffusion kurtosis imaging. Eur Radiol 2021; 31(11): 8388-98.
[http://dx.doi.org/10.1007/s00330-021-07966-y] [PMID: 33884473]
[24]
Yuan Y, Zeng D, Liu Y, et al. DWI and IVIM are predictors of Ki67 proliferation index: direct comparison of MRI images and pathological slices in a murine model of rhabdomyosarcoma. Eur Radiol 2020; 30(3): 1334-41.
[http://dx.doi.org/10.1007/s00330-019-06509-w] [PMID: 31705255]
[25]
Jiang X, Jia H, Zhang Z, Wei C, Wang C, Dong J. The feasibility of combining ADC value with texture analysis of T2WI, DWI and CE-T1WI to preoperatively predict the expression levels of Ki-67 and p53 of endometrial carcinoma. Front Oncol 2022; 11805545
[http://dx.doi.org/10.3389/fonc.2021.805545] [PMID: 35127515]
[26]
Liu Y, Wang X, Cui Y, et al. Comparative study of monoexponential, intravoxel incoherent motion, kurtosis, and ivim-kurtosis models for the diagnosis and aggressiveness assessment of prostate cancer. Front Oncol 2020; 10: 1763.
[http://dx.doi.org/10.3389/fonc.2020.01763] [PMID: 33042822]
[27]
Xiao Z, Zhong Y, Tang Z, et al. Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status. Eur Radiol 2018; 28(7): 2923-33.
[http://dx.doi.org/10.1007/s00330-017-5286-x] [PMID: 29383521]
[28]
Zhang J, Suo S, Liu G, et al. Comparison of monoexponential, biexponential, stretched-exponential, and kurtosis models of diffusion-weighted imaging in differentiation of renal solid masses. Korean J Radiol 2019; 20(5): 791-800.
[http://dx.doi.org/10.3348/kjr.2018.0474] [PMID: 30993930]
[29]
Pang Y, Turkbey B, Bernardo M, et al. Intravoxel incoherent motion MR imaging for prostate cancer: An evaluation of perfusion fraction and diffusion coefficient derived from different b -value combinations. Magn Reson Med 2013; 69(2): 553-62.
[http://dx.doi.org/10.1002/mrm.24277] [PMID: 22488794]
[30]
Qi XX, Shi DF, Ren SX, et al. Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery. Eur Radiol 2018; 28(4): 1748-55.
[http://dx.doi.org/10.1007/s00330-017-5108-1] [PMID: 29143940]
[31]
Zhang Q, Yu X, Ouyang H, et al. Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: A preliminary study. Eur Radiol 2021; 31(8): 5576-85.
[http://dx.doi.org/10.1007/s00330-020-07612-z] [PMID: 33464399]
[32]
Yin J, Sun H, Wang Z, Ni H, Shen W, Sun PZ. Diffusion kurtosis imaging of acute infarction: Comparison with routine diffusion and follow-up MR imaging. Radiology 2018; 287(2): 651-7.
[http://dx.doi.org/10.1148/radiol.2017170553] [PMID: 29558293]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy